Add thinning (#18)

* Add thinning

* remove commented code

* use dims

* ignore dims if factor is auto

* fix dims

src/arviz_stats/accessors.py

@@ -51,6 +51,10 @@ def kde(self, dims=None, **kwargs):
         """Compute the KDE on the DataArray."""
         return get_function("kde")(self._obj, dims=dims, **kwargs)
 
+    def thin(self, factor="auto", dims=None, **kwargs):
+        """Perform thinning on the DataArray."""
+        return get_function("thin")(self._obj, factor=factor, dims=dims, **kwargs)
+
 
 @xr.register_dataset_accessor("azstats")
 class AzStatsDsAccessor(_BaseAccessor):
@@ -135,6 +139,10 @@ def ecdf(self, dims=None, **kwargs):
         # TODO: implement ecdf here so it doesn't depend on numba
         return self._apply(ecdf, dims=dims, **kwargs).rename(ecdf_axis="plot_axis")
 
+    def thin(self, dims=None, factor="auto"):
+        """Perform thinning for all the variables in the dataset."""
+        return self._apply(get_function("thin"), dims=dims, factor=factor)
+
 
 @register_datatree_accessor("azstats")
 class AzStatsDtAccessor(_BaseAccessor):
@@ -193,3 +201,7 @@ def kde(self, dims=None, group="posterior", **kwargs):
     def histogram(self, dims=None, group="posterior", **kwargs):
         """Compute the KDE for all variables in a group of the DataTree."""
         return self._apply("histogram", dims=dims, group=group, **kwargs)
+
+    def thin(self, dims=None, group="posterior", **kwargs):
+        """Perform thinning for all variables in a group of the DataTree."""
+        return self._apply("thin", dims=dims, group=group, **kwargs)

src/arviz_stats/base/dataarray.py

@@ -3,6 +3,8 @@
 "dataarray" functions take :class:`xarray.DataArray` as inputs.
 """
 
+import warnings
+
 import numpy as np
 from arviz_base import rcParams
 from xarray import DataArray, apply_ufunc, concat
@@ -193,5 +195,36 @@ def kde(self, da, dims=None, circular=False, grid_len=512, **kwargs):
         out = concat((grid, pdf), dim=plot_axis)
         return out.assign_coords({"bw" if da.name is None else f"bw_{da.name}": bw})
 
+    def thin(self, da, factor="auto", dims=None):
+        """Perform thinning on DataArray input."""
+        if factor == "auto" and dims is not None:
+            warnings.warn("dims are ignored if factor is auto")
+
+        if factor == "auto":
+            n_samples = da.sizes["chain"] * da.sizes["draw"]
+            ess_ave = np.minimum(
+                self.ess(da, method="bulk", dims=["chain", "draw"]),
+                self.ess(da, method="tail", dims=["chain", "draw"]),
+            ).mean()
+            factor = int(np.ceil(n_samples / ess_ave))
+            dims = "draw"
+
+        elif isinstance(factor, (float | int)):
+            if dims is None:
+                dims = rcParams["data.sample_dims"]
+            if not isinstance(dims, str):
+                if len(dims) >= 2:
+                    raise ValueError("dims must be of length 1")
+                if len(dims) == 1:
+                    dims = dims[0]
+
+            factor = int(factor)
+            if factor == 1:
+                return da
+            if factor < 1:
+                raise ValueError("factor must be greater than 1")
+
+        return da.sel({dims: slice(None, None, factor)})
+
 
 dataarray_stats = BaseDataArray(array_class=array_stats)